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8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

December 1–3, 2014 | Boston, Massachusetts, United States


Bio-Inspired Game Theory: The Case of Physarum Polycephalum

Andrew Schumann (University of Information Technology and Management in Rzeszow, Poland), Krzysztof Pancerz (University of Information Technology and Management in Rzeszow, Poland), Andrew Adamatzky (University of the West of England, UK) and Martin Grube (University of Graz, Austria)

In this paper, first, we show that the true slime mould (plasmodial stage of Physarum polycephalum) is a natural transition system which can be considered a biological model for concurrent games, i.e. it can simulate the game semantics in the form of concurrent games. Second, we extend the notion of concurrent games to context-based games and show that this new form of games is a game semantics that is more suitable for the implementation multi-agent games in the slime mould behavior. The notion of context-based games as strong extension of concurrent games is introduced for the first time. Games on the medium of one-cell organism are defined for the first time, too. In context-based games, we appeal to the following game-theoretic assumptions: (i) each game can be assumed infinite, because its rules can change; (ii) players can change their strategies and the set of actions is infinite for each player; (iii) resistance points for players are reduced to the payoffs if all actions are well-founded; (iv) for any game there is performative efficiency, when hybrid actions of players belong to the interval of expected modifications. Logic circuits on the medium of slime mould can be designed in the form of context-based games.


Feature ranking in transcriptional networks: Packet receipt as a dynamical metric

Bhanu Kamapantula (Virginia Commonwealth University, USA), Michael Mayo (US Army Engineer Research and Development Center, USA), Edward Perkins (US Army Engineer Research and Development Center, USA), Ahmed Abdelzaher (Virginia Commonwealth University, USA), and Preetam Ghosh (Virginia Commonwealth University, USA)

Machine learning techniques may be useful in determining the features contributing to some biological properties, such as robustness, which is the tendency for biological systems to resist a change of state. In this work, we compare transcriptional subnetworks extracted from the bacterium Escherichia coli and the baker’s yeast Saccharomyces cerevisiae using in silico experiments. We use the packet receipt rate as a metric to quantify biological robustness, which is different from the usual structural metrics since it captures the dynamic behavior of the network. We define seventeen features based on structural significance, such as transcriptional motifs, and conventional metrics, such as average shortest path and network density, among others. Feature ranking is performed, based on a grid-search method to identify Support Vector Machine classifier parameters using cross validation. Our results indicate that feed-forward loop based features are important for bacterial transcriptional networks, whereas network density, degree-centrality based and bifan-based features are found to be significant for yeast-derived transcriptional networks. Interestingly, results suggest that feature significance varies with network size (number of nodes). As a first, this study quantifies the impact of the feed-forward loop and bifan transcriptional motif abundance observed in natural networks.